{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multivariate Time Series\n",
    "In this notebook, we show examples of how multivariate time series can be created and used in darts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fix python path if working locally\n",
    "from utils import fix_pythonpath_if_working_locally\n",
    "fix_pythonpath_if_working_locally()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from darts import TimeSeries\n",
    "from darts.models.tcn_model import TCNModel\n",
    "from darts.dataprocessing.transformers import Scaler\n",
    "from darts.metrics import r2_score\n",
    "import darts.utils.timeseries_generation as tg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start by creating two univariate TimeSeries instances and combining them to form a multivariate one using the TimeSeries.stack method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "series_a = tg.sine_timeseries(length=50)\n",
    "series_b = tg.linear_timeseries(length=50)\n",
    "series_ab = series_a.stack(series_b)\n",
    "series_ab.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Any individual 'component' of a multivariate TimeSeries instance can be retrieved using the TimeSeries.univariate_component method which takes a 0-indexed integer as an argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "series_ab.univariate_component(0).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following we will use real-world univariate data and see how we can expand upon it by creating multivariate TimeSeries objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Read data:\n",
    "df = pd.read_csv('AirPassengers.csv', delimiter=\",\")\n",
    "ts = TimeSeries.from_dataframe(df, 'Month', ['#Passengers'])\n",
    "ts.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Any univariate or multivariate TimeSeries instance can easily be augmented by additional components that are based on the datetime index. Any integer-valued attribute of pd.DatetimeIndex can be added, such as the number of the month, as shown below. Before plotting, the data is normalized so that all features are on the same scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "series_with_month = ts.add_datetime_attribute('month')\n",
    "Scaler().fit_transform(series_with_month).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, we can add a binary time series which indicates whether the current point on the time index represents a holiday in a specific country. Since the above time series is monthly, we shall use the artificial time series from before as an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "series_with_holidays = series_a.add_holidays('US')\n",
    "Scaler().fit_transform(series_with_holidays).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, how can we make use of such augmentations of TimeSeries instances? To see where it can help, let's look at the performance of a TCN forecasting model on the above air passengers data set. First let's fit the TCN model on the univariate series for 200 epochs and predict 13 steps into the future."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "[2020-11-09 10:47:02,159] INFO | darts.models.tcn_model | Number of layers chosen: 2\n",
      "[2020-11-09 10:47:02,159] INFO | darts.models.tcn_model | Number of layers chosen: 2\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "HBox(children=(FloatProgress(value=0.0, max=300.0), HTML(value='')))",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "6b85165462224c9286fae8e891859cd0"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Create training and validation sets:\n",
    "train, val = ts.split_after(pd.Timestamp('19580801'))\n",
    "transformer = Scaler()\n",
    "train_transformed = transformer.fit_transform(train)\n",
    "val_transformed = transformer.transform(val)\n",
    "ts_transformed = transformer.transform(ts)\n",
    "\n",
    "# Create TCNModel instance\n",
    "my_model = TCNModel(\n",
    "    n_epochs=300,\n",
    "    input_length=15,\n",
    "    output_length=13,\n",
    "    dropout=0.1,\n",
    "    dilation_base=2,\n",
    "    weight_norm=True,\n",
    "    kernel_size=4,\n",
    "    num_filters=3,\n",
    "    random_state=0\n",
    ")\n",
    "\n",
    "# Fit model\n",
    "my_model.fit(train_transformed, val_training_series=val_transformed, verbose=True)\n",
    "\n",
    "# Evaluate model\n",
    "def eval_model(model):\n",
    "    pred_series = model.predict(13, True)\n",
    "    plt.figure(figsize=(8,5))\n",
    "    ts_transformed.univariate_component(0).plot(label='actual')\n",
    "    pred_series.plot(label='forecast')\n",
    "    plt.title('R2: {}'.format(r2_score(val_transformed.univariate_component(0), pred_series.slice_intersect(val_transformed))))\n",
    "    plt.legend();\n",
    "eval_model(my_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Limited to 20 epochs, we do not get a very good performance. Let's try the same thing but with an enhanced series that contains information about the month. Here, we also pass the second positional argument to the 'add_datetime_attribute' method which refers to whether the datetime attribute should be encoded in a one-hot fashion or not. Note that for predictions of multivariate TimeSeries instances, the maximum number of steps that can be predicted into the future is equal to TorchForecastingModel.output_length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "[2020-11-09 10:47:13,405] INFO | darts.models.tcn_model | Number of layers chosen: 2\n",
      "[2020-11-09 10:47:13,405] INFO | darts.models.tcn_model | Number of layers chosen: 2\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "HBox(children=(FloatProgress(value=0.0, max=300.0), HTML(value='')))",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "d75a6e40ae50456186a45006fe8bca01"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 576x360 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Create training and validation sets:\n",
    "ts_enhanced = ts.add_datetime_attribute('month', True)\n",
    "train, val = ts_enhanced.split_after(pd.Timestamp('19580801'))\n",
    "transformer = Scaler()\n",
    "train_transformed = transformer.fit_transform(train)\n",
    "val_transformed = transformer.transform(val)\n",
    "ts_transformed = transformer.transform(ts_enhanced)\n",
    "\n",
    "# Create TCNModel instance\n",
    "my_model = TCNModel(\n",
    "    input_size=13,\n",
    "    n_epochs=300,\n",
    "    input_length=15,\n",
    "    output_length=13,\n",
    "    dropout=0.1,\n",
    "    dilation_base=2,\n",
    "    weight_norm=True,\n",
    "    kernel_size=4,\n",
    "    num_filters=3,\n",
    "    random_state=0\n",
    ")\n",
    "\n",
    "# Fit model\n",
    "my_model.fit(\n",
    "    target_series=train_transformed['0'],\n",
    "    training_series=train_transformed,\n",
    "    val_target_series=val_transformed['0'],\n",
    "    val_training_series=val_transformed,\n",
    "    verbose=True\n",
    ")\n",
    "\n",
    "#Evaluate model\n",
    "eval_model(my_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that with this limited number of training epochs, we get a better performance out of the model that uses the multivariate series as input."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}